Overview

Dataset statistics

Number of variables40
Number of observations4882
Missing cells8951
Missing cells (%)4.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 MiB
Average record size in memory320.0 B

Variable types

Numeric30
Categorical10

Alerts

indfmt has constant value ""Constant
consol has constant value ""Constant
popsrc has constant value ""Constant
datafmt has constant value ""Constant
curcd has constant value ""Constant
costat has constant value ""Constant
datadate has a high cardinality: 121 distinct valuesHigh cardinality
tic has a high cardinality: 498 distinct valuesHigh cardinality
cusip has a high cardinality: 498 distinct valuesHigh cardinality
conm has a high cardinality: 498 distinct valuesHigh cardinality
gvkey is highly overall correlated with cikHigh correlation
act is highly overall correlated with at and 17 other fieldsHigh correlation
at is highly overall correlated with act and 16 other fieldsHigh correlation
bkvlps is highly overall correlated with ceql and 2 other fieldsHigh correlation
capx is highly overall correlated with act and 15 other fieldsHigh correlation
ceql is highly overall correlated with act and 16 other fieldsHigh correlation
csho is highly overall correlated with act and 16 other fieldsHigh correlation
dltt is highly overall correlated with act and 15 other fieldsHigh correlation
dt is highly overall correlated with act and 16 other fieldsHigh correlation
dvp is highly overall correlated with pstklHigh correlation
ebitda is highly overall correlated with act and 16 other fieldsHigh correlation
emp is highly overall correlated with act and 12 other fieldsHigh correlation
lct is highly overall correlated with act and 16 other fieldsHigh correlation
lt is highly overall correlated with act and 16 other fieldsHigh correlation
ni is highly overall correlated with act and 15 other fieldsHigh correlation
ppegt is highly overall correlated with act and 16 other fieldsHigh correlation
pstkl is highly overall correlated with dvpHigh correlation
revt is highly overall correlated with act and 16 other fieldsHigh correlation
seq is highly overall correlated with act and 16 other fieldsHigh correlation
teq is highly overall correlated with act and 16 other fieldsHigh correlation
xad is highly overall correlated with act and 16 other fieldsHigh correlation
xrd is highly overall correlated with actHigh correlation
cik is highly overall correlated with gvkeyHigh correlation
mkvalt is highly overall correlated with act and 16 other fieldsHigh correlation
act has 864 (17.7%) missing valuesMissing
bkvlps has 70 (1.4%) missing valuesMissing
csho has 69 (1.4%) missing valuesMissing
dt has 548 (11.2%) missing valuesMissing
ebitda has 264 (5.4%) missing valuesMissing
emp has 71 (1.5%) missing valuesMissing
lct has 860 (17.6%) missing valuesMissing
ppegt has 522 (10.7%) missing valuesMissing
xad has 2720 (55.7%) missing valuesMissing
xrd has 2053 (42.1%) missing valuesMissing
dvpsp_f has 93 (1.9%) missing valuesMissing
mkvalt has 401 (8.2%) missing valuesMissing
prcc_f has 106 (2.2%) missing valuesMissing
bkvlps is highly skewed (γ1 = 47.36237195)Skewed
tic is uniformly distributedUniform
cusip is uniformly distributedUniform
conm is uniformly distributedUniform
capx has 309 (6.3%) zerosZeros
dltt has 258 (5.3%) zerosZeros
dt has 237 (4.9%) zerosZeros
dvp has 4213 (86.3%) zerosZeros
pstkl has 4147 (84.9%) zerosZeros
xrd has 651 (13.3%) zerosZeros
dvpsp_f has 1110 (22.7%) zerosZeros

Reproduction

Analysis started2023-05-08 04:16:06.635156
Analysis finished2023-05-08 04:17:33.281181
Duration1 minute and 26.65 seconds
Software versionydata-profiling vv4.0.0
Download configurationconfig.json

Variables

gvkey
Real number (ℝ)

Distinct498
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46174.04
Minimum1045
Maximum316056
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.3 KiB
2023-05-08T00:17:33.372195image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1045
5-th percentile2019
Q16268
median12587.5
Q363172
95-th percentile177267
Maximum316056
Range315011
Interquartile range (IQR)56904

Descriptive statistics

Standard deviation61447.241
Coefficient of variation (CV)1.3307746
Kurtosis1.0336797
Mean46174.04
Median Absolute Deviation (MAD)9770.5
Skewness1.4633294
Sum2.2542166 × 108
Variance3.7757634 × 109
MonotonicityIncreasing
2023-05-08T00:17:33.476727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6502 11
 
0.2%
11259 11
 
0.2%
3813 11
 
0.2%
141913 11
 
0.2%
9248 11
 
0.2%
157855 11
 
0.2%
162129 11
 
0.2%
165123 11
 
0.2%
117768 11
 
0.2%
2184 11
 
0.2%
Other values (488) 4772
97.7%
ValueCountFrequency (%)
1045 10
0.2%
1075 10
0.2%
1078 10
0.2%
1161 10
0.2%
1209 10
0.2%
1230 10
0.2%
1300 10
0.2%
1327 10
0.2%
1380 10
0.2%
1440 10
0.2%
ValueCountFrequency (%)
316056 9
0.2%
294524 10
0.2%
260774 10
0.2%
189491 10
0.2%
187697 10
0.2%
187450 10
0.2%
186989 10
0.2%
186310 10
0.2%
185532 10
0.2%
184996 10
0.2%

datadate
Categorical

Distinct121
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size38.3 KiB
2019-12-31
386 
2018-12-31
384 
2017-12-31
384 
2016-12-31
382 
2015-12-31
379 
Other values (116)
2967 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters48820
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2010-12-31
2nd row2011-12-31
3rd row2012-12-31
4th row2013-12-31
5th row2014-12-31

Common Values

ValueCountFrequency (%)
2019-12-31 386
 
7.9%
2018-12-31 384
 
7.9%
2017-12-31 384
 
7.9%
2016-12-31 382
 
7.8%
2015-12-31 379
 
7.8%
2014-12-31 373
 
7.6%
2013-12-31 370
 
7.6%
2012-12-31 361
 
7.4%
2011-12-31 357
 
7.3%
2010-12-31 354
 
7.3%
Other values (111) 1152
23.6%

Length

2023-05-08T00:17:33.567410image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2019-12-31 386
 
7.9%
2017-12-31 384
 
7.9%
2018-12-31 384
 
7.9%
2016-12-31 382
 
7.8%
2015-12-31 379
 
7.8%
2014-12-31 373
 
7.6%
2013-12-31 370
 
7.6%
2012-12-31 361
 
7.4%
2011-12-31 357
 
7.3%
2010-12-31 354
 
7.3%
Other values (111) 1152
23.6%

Most occurring characters

ValueCountFrequency (%)
1 13740
28.1%
- 9764
20.0%
2 9149
18.7%
0 7030
14.4%
3 5435
 
11.1%
9 751
 
1.5%
6 718
 
1.5%
5 592
 
1.2%
8 573
 
1.2%
7 536
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 39056
80.0%
Dash Punctuation 9764
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 13740
35.2%
2 9149
23.4%
0 7030
18.0%
3 5435
 
13.9%
9 751
 
1.9%
6 718
 
1.8%
5 592
 
1.5%
8 573
 
1.5%
7 536
 
1.4%
4 532
 
1.4%
Dash Punctuation
ValueCountFrequency (%)
- 9764
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 48820
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 13740
28.1%
- 9764
20.0%
2 9149
18.7%
0 7030
14.4%
3 5435
 
11.1%
9 751
 
1.5%
6 718
 
1.5%
5 592
 
1.2%
8 573
 
1.2%
7 536
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48820
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 13740
28.1%
- 9764
20.0%
2 9149
18.7%
0 7030
14.4%
3 5435
 
11.1%
9 751
 
1.5%
6 718
 
1.5%
5 592
 
1.2%
8 573
 
1.2%
7 536
 
1.1%

fyear
Real number (ℝ)

Distinct11
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2014.49
Minimum2009
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.3 KiB
2023-05-08T00:17:33.634782image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2009
5-th percentile2010
Q12012
median2015
Q32017
95-th percentile2019
Maximum2019
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8865742
Coefficient of variation (CV)0.0014329057
Kurtosis-1.195576
Mean2014.49
Median Absolute Deviation (MAD)2
Skewness-0.031825149
Sum9834740
Variance8.3323106
MonotonicityNot monotonic
2023-05-08T00:17:33.703139image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2017 496
10.2%
2018 496
10.2%
2016 495
10.1%
2015 493
10.1%
2014 488
10.0%
2013 484
9.9%
2012 477
9.8%
2019 473
9.7%
2011 471
9.6%
2010 468
9.6%
ValueCountFrequency (%)
2009 41
 
0.8%
2010 468
9.6%
2011 471
9.6%
2012 477
9.8%
2013 484
9.9%
2014 488
10.0%
2015 493
10.1%
2016 495
10.1%
2017 496
10.2%
2018 496
10.2%
ValueCountFrequency (%)
2019 473
9.7%
2018 496
10.2%
2017 496
10.2%
2016 495
10.1%
2015 493
10.1%
2014 488
10.0%
2013 484
9.9%
2012 477
9.8%
2011 471
9.6%
2010 468
9.6%

indfmt
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.3 KiB
INDL
4882 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters19528
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowINDL
2nd rowINDL
3rd rowINDL
4th rowINDL
5th rowINDL

Common Values

ValueCountFrequency (%)
INDL 4882
100.0%

Length

2023-05-08T00:17:33.774611image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-08T00:17:33.841706image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
indl 4882
100.0%

Most occurring characters

ValueCountFrequency (%)
I 4882
25.0%
N 4882
25.0%
D 4882
25.0%
L 4882
25.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 19528
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 4882
25.0%
N 4882
25.0%
D 4882
25.0%
L 4882
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 19528
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 4882
25.0%
N 4882
25.0%
D 4882
25.0%
L 4882
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19528
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 4882
25.0%
N 4882
25.0%
D 4882
25.0%
L 4882
25.0%

consol
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.3 KiB
C
4882 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4882
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowC
3rd rowC
4th rowC
5th rowC

Common Values

ValueCountFrequency (%)
C 4882
100.0%

Length

2023-05-08T00:17:33.901834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-08T00:17:33.967696image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
c 4882
100.0%

Most occurring characters

ValueCountFrequency (%)
C 4882
100.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4882
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 4882
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4882
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 4882
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4882
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 4882
100.0%

popsrc
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.3 KiB
D
4882 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4882
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD
2nd rowD
3rd rowD
4th rowD
5th rowD

Common Values

ValueCountFrequency (%)
D 4882
100.0%

Length

2023-05-08T00:17:34.022629image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-08T00:17:34.088144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
d 4882
100.0%

Most occurring characters

ValueCountFrequency (%)
D 4882
100.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4882
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 4882
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4882
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 4882
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4882
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 4882
100.0%

datafmt
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.3 KiB
STD
4882 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters14646
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSTD
2nd rowSTD
3rd rowSTD
4th rowSTD
5th rowSTD

Common Values

ValueCountFrequency (%)
STD 4882
100.0%

Length

2023-05-08T00:17:34.142729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-08T00:17:34.208346image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
std 4882
100.0%

Most occurring characters

ValueCountFrequency (%)
S 4882
33.3%
T 4882
33.3%
D 4882
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 14646
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 4882
33.3%
T 4882
33.3%
D 4882
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 14646
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 4882
33.3%
T 4882
33.3%
D 4882
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14646
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 4882
33.3%
T 4882
33.3%
D 4882
33.3%

tic
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct498
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Memory size38.3 KiB
KR
 
11
WMT
 
11
TGT
 
11
GPN
 
11
ROST
 
11
Other values (493)
4827 

Length

Max length5
Median length3
Mean length3.1519869
Min length1

Characters and Unicode

Total characters15388
Distinct characters27
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowAAL
2nd rowAAL
3rd rowAAL
4th rowAAL
5th rowAAL

Common Values

ValueCountFrequency (%)
KR 11
 
0.2%
WMT 11
 
0.2%
TGT 11
 
0.2%
GPN 11
 
0.2%
ROST 11
 
0.2%
CRM 11
 
0.2%
MOS 11
 
0.2%
LDOS 11
 
0.2%
NVDA 11
 
0.2%
BBY 11
 
0.2%
Other values (488) 4772
97.7%

Length

2023-05-08T00:17:34.272210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kr 11
 
0.2%
dltr 11
 
0.2%
wmt 11
 
0.2%
tjx 11
 
0.2%
low 11
 
0.2%
bbwi 11
 
0.2%
adsk 11
 
0.2%
hd 11
 
0.2%
ulta 11
 
0.2%
khc 11
 
0.2%
Other values (488) 4772
97.7%

Most occurring characters

ValueCountFrequency (%)
A 1120
 
7.3%
C 1089
 
7.1%
T 979
 
6.4%
M 929
 
6.0%
S 925
 
6.0%
R 860
 
5.6%
L 834
 
5.4%
P 790
 
5.1%
E 786
 
5.1%
N 749
 
4.9%
Other values (17) 6327
41.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 15368
99.9%
Other Punctuation 20
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 1120
 
7.3%
C 1089
 
7.1%
T 979
 
6.4%
M 929
 
6.0%
S 925
 
6.0%
R 860
 
5.6%
L 834
 
5.4%
P 790
 
5.1%
E 786
 
5.1%
N 749
 
4.9%
Other values (16) 6307
41.0%
Other Punctuation
ValueCountFrequency (%)
. 20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 15368
99.9%
Common 20
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 1120
 
7.3%
C 1089
 
7.1%
T 979
 
6.4%
M 929
 
6.0%
S 925
 
6.0%
R 860
 
5.6%
L 834
 
5.4%
P 790
 
5.1%
E 786
 
5.1%
N 749
 
4.9%
Other values (16) 6307
41.0%
Common
ValueCountFrequency (%)
. 20
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15388
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 1120
 
7.3%
C 1089
 
7.1%
T 979
 
6.4%
M 929
 
6.0%
S 925
 
6.0%
R 860
 
5.6%
L 834
 
5.4%
P 790
 
5.1%
E 786
 
5.1%
N 749
 
4.9%
Other values (17) 6327
41.1%

cusip
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct498
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Memory size38.3 KiB
501044101
 
11
931142103
 
11
87612E106
 
11
37940X102
 
11
778296103
 
11
Other values (493)
4827 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters43938
Distinct characters33
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row02376R102
2nd row02376R102
3rd row02376R102
4th row02376R102
5th row02376R102

Common Values

ValueCountFrequency (%)
501044101 11
 
0.2%
931142103 11
 
0.2%
87612E106 11
 
0.2%
37940X102 11
 
0.2%
778296103 11
 
0.2%
79466L302 11
 
0.2%
61945C103 11
 
0.2%
525327102 11
 
0.2%
67066G104 11
 
0.2%
086516101 11
 
0.2%
Other values (488) 4772
97.7%

Length

2023-05-08T00:17:34.351556image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
501044101 11
 
0.2%
256746108 11
 
0.2%
931142103 11
 
0.2%
872540109 11
 
0.2%
548661107 11
 
0.2%
070830104 11
 
0.2%
052769106 11
 
0.2%
437076102 11
 
0.2%
90384s303 11
 
0.2%
500754106 11
 
0.2%
Other values (488) 4772
97.7%

Most occurring characters

ValueCountFrequency (%)
0 8518
19.4%
1 7952
18.1%
4 3472
7.9%
2 3383
 
7.7%
3 3350
 
7.6%
5 3257
 
7.4%
6 3220
 
7.3%
7 2905
 
6.6%
9 2853
 
6.5%
8 2828
 
6.4%
Other values (23) 2200
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 41738
95.0%
Uppercase Letter 2200
 
5.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 283
 
12.9%
L 170
 
7.7%
V 134
 
6.1%
C 132
 
6.0%
R 130
 
5.9%
E 121
 
5.5%
T 120
 
5.5%
P 120
 
5.5%
X 111
 
5.0%
H 100
 
4.5%
Other values (13) 779
35.4%
Decimal Number
ValueCountFrequency (%)
0 8518
20.4%
1 7952
19.1%
4 3472
8.3%
2 3383
 
8.1%
3 3350
 
8.0%
5 3257
 
7.8%
6 3220
 
7.7%
7 2905
 
7.0%
9 2853
 
6.8%
8 2828
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
Common 41738
95.0%
Latin 2200
 
5.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 283
 
12.9%
L 170
 
7.7%
V 134
 
6.1%
C 132
 
6.0%
R 130
 
5.9%
E 121
 
5.5%
T 120
 
5.5%
P 120
 
5.5%
X 111
 
5.0%
H 100
 
4.5%
Other values (13) 779
35.4%
Common
ValueCountFrequency (%)
0 8518
20.4%
1 7952
19.1%
4 3472
8.3%
2 3383
 
8.1%
3 3350
 
8.0%
5 3257
 
7.8%
6 3220
 
7.7%
7 2905
 
7.0%
9 2853
 
6.8%
8 2828
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43938
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8518
19.4%
1 7952
18.1%
4 3472
7.9%
2 3383
 
7.7%
3 3350
 
7.6%
5 3257
 
7.4%
6 3220
 
7.3%
7 2905
 
6.6%
9 2853
 
6.5%
8 2828
 
6.4%
Other values (23) 2200
 
5.0%

conm
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct498
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Memory size38.3 KiB
KROGER CO
 
11
WALMART INC
 
11
TARGET CORP
 
11
GLOBAL PAYMENTS INC
 
11
ROSS STORES INC
 
11
Other values (493)
4827 

Length

Max length28
Median length21
Mean length17.226137
Min length5

Characters and Unicode

Total characters84098
Distinct characters37
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowAMERICAN AIRLINES GROUP INC
2nd rowAMERICAN AIRLINES GROUP INC
3rd rowAMERICAN AIRLINES GROUP INC
4th rowAMERICAN AIRLINES GROUP INC
5th rowAMERICAN AIRLINES GROUP INC

Common Values

ValueCountFrequency (%)
KROGER CO 11
 
0.2%
WALMART INC 11
 
0.2%
TARGET CORP 11
 
0.2%
GLOBAL PAYMENTS INC 11
 
0.2%
ROSS STORES INC 11
 
0.2%
SALESFORCE INC 11
 
0.2%
MOSAIC CO 11
 
0.2%
LEIDOS HOLDINGS INC 11
 
0.2%
NVIDIA CORP 11
 
0.2%
BEST BUY CO INC 11
 
0.2%
Other values (488) 4772
97.7%

Length

2023-05-08T00:17:34.433315image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
inc 2501
 
18.2%
corp 1124
 
8.2%
co 471
 
3.4%
group 228
 
1.7%
212
 
1.5%
energy 180
 
1.3%
plc 139
 
1.0%
financial 136
 
1.0%
technologies 125
 
0.9%
holdings 114
 
0.8%
Other values (703) 8522
62.0%

Most occurring characters

ValueCountFrequency (%)
8900
10.6%
C 7452
 
8.9%
N 7331
 
8.7%
I 6905
 
8.2%
E 6580
 
7.8%
O 6289
 
7.5%
R 6062
 
7.2%
A 5138
 
6.1%
T 4118
 
4.9%
S 4100
 
4.9%
Other values (27) 21223
25.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 74317
88.4%
Space Separator 8900
 
10.6%
Other Punctuation 353
 
0.4%
Dash Punctuation 196
 
0.2%
Open Punctuation 141
 
0.2%
Close Punctuation 141
 
0.2%
Decimal Number 50
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 7452
10.0%
N 7331
9.9%
I 6905
9.3%
E 6580
 
8.9%
O 6289
 
8.5%
R 6062
 
8.2%
A 5138
 
6.9%
T 4118
 
5.5%
S 4100
 
5.5%
L 3778
 
5.1%
Other values (16) 16564
22.3%
Other Punctuation
ValueCountFrequency (%)
& 242
68.6%
' 51
 
14.4%
. 40
 
11.3%
/ 20
 
5.7%
Decimal Number
ValueCountFrequency (%)
6 20
40.0%
3 20
40.0%
5 10
20.0%
Space Separator
ValueCountFrequency (%)
8900
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 196
100.0%
Open Punctuation
ValueCountFrequency (%)
( 141
100.0%
Close Punctuation
ValueCountFrequency (%)
) 141
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 74317
88.4%
Common 9781
 
11.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 7452
10.0%
N 7331
9.9%
I 6905
9.3%
E 6580
 
8.9%
O 6289
 
8.5%
R 6062
 
8.2%
A 5138
 
6.9%
T 4118
 
5.5%
S 4100
 
5.5%
L 3778
 
5.1%
Other values (16) 16564
22.3%
Common
ValueCountFrequency (%)
8900
91.0%
& 242
 
2.5%
- 196
 
2.0%
( 141
 
1.4%
) 141
 
1.4%
' 51
 
0.5%
. 40
 
0.4%
/ 20
 
0.2%
6 20
 
0.2%
3 20
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 84098
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8900
10.6%
C 7452
 
8.9%
N 7331
 
8.7%
I 6905
 
8.2%
E 6580
 
7.8%
O 6289
 
7.5%
R 6062
 
7.2%
A 5138
 
6.1%
T 4118
 
4.9%
S 4100
 
4.9%
Other values (27) 21223
25.2%

curcd
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.3 KiB
USD
4882 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters14646
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUSD
2nd rowUSD
3rd rowUSD
4th rowUSD
5th rowUSD

Common Values

ValueCountFrequency (%)
USD 4882
100.0%

Length

2023-05-08T00:17:34.512131image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-08T00:17:34.578078image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
usd 4882
100.0%

Most occurring characters

ValueCountFrequency (%)
U 4882
33.3%
S 4882
33.3%
D 4882
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 14646
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 4882
33.3%
S 4882
33.3%
D 4882
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 14646
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 4882
33.3%
S 4882
33.3%
D 4882
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14646
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 4882
33.3%
S 4882
33.3%
D 4882
33.3%

fyr
Real number (ℝ)

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.624334
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.3 KiB
2023-05-08T00:17:34.632850image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q112
median12
Q312
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.897732
Coefficient of variation (CV)0.27274481
Kurtosis3.3461639
Mean10.624334
Median Absolute Deviation (MAD)0
Skewness-2.113947
Sum51868
Variance8.3968509
MonotonicityNot monotonic
2023-05-08T00:17:34.701110image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
12 3730
76.4%
9 248
 
5.1%
6 222
 
4.5%
1 168
 
3.4%
10 115
 
2.4%
5 98
 
2.0%
3 90
 
1.8%
8 60
 
1.2%
4 44
 
0.9%
11 44
 
0.9%
Other values (2) 63
 
1.3%
ValueCountFrequency (%)
1 168
3.4%
2 23
 
0.5%
3 90
 
1.8%
4 44
 
0.9%
5 98
 
2.0%
6 222
4.5%
7 40
 
0.8%
8 60
 
1.2%
9 248
5.1%
10 115
2.4%
ValueCountFrequency (%)
12 3730
76.4%
11 44
 
0.9%
10 115
 
2.4%
9 248
 
5.1%
8 60
 
1.2%
7 40
 
0.8%
6 222
 
4.5%
5 98
 
2.0%
4 44
 
0.9%
3 90
 
1.8%

act
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3933
Distinct (%)97.9%
Missing864
Missing (%)17.7%
Infinite0
Infinite (%)0.0%
Mean8405.045
Minimum25.037
Maximum175552
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.3 KiB
2023-05-08T00:17:34.790289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum25.037
5-th percentile511.6895
Q11581.8093
median3325.052
Q38251.25
95-th percentile34450.7
Maximum175552
Range175526.96
Interquartile range (IQR)6669.4407

Descriptive statistics

Standard deviation14911.298
Coefficient of variation (CV)1.774089
Kurtosis30.076035
Mean8405.045
Median Absolute Deviation (MAD)2255.3515
Skewness4.6199071
Sum33771471
Variance2.223468 × 108
MonotonicityNot monotonic
2023-05-08T00:17:34.887077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1527 3
 
0.1%
13704 2
 
< 0.1%
2730 2
 
< 0.1%
3507 2
 
< 0.1%
2971.9 2
 
< 0.1%
3033 2
 
< 0.1%
4404 2
 
< 0.1%
3836 2
 
< 0.1%
3465 2
 
< 0.1%
10566 2
 
< 0.1%
Other values (3923) 3997
81.9%
(Missing) 864
 
17.7%
ValueCountFrequency (%)
25.037 1
< 0.1%
30.933 1
< 0.1%
44.173 1
< 0.1%
47.492 1
< 0.1%
50.275 1
< 0.1%
52.956 1
< 0.1%
61.968 1
< 0.1%
66.025 1
< 0.1%
69.273 1
< 0.1%
76.785 1
< 0.1%
ValueCountFrequency (%)
175552 1
< 0.1%
169662 1
< 0.1%
162819 1
< 0.1%
159851 1
< 0.1%
152578 1
< 0.1%
139660 1
< 0.1%
135676 1
< 0.1%
131339 1
< 0.1%
128645 1
< 0.1%
124712 1
< 0.1%

at
Real number (ℝ)

Distinct4833
Distinct (%)99.5%
Missing23
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean62633.461
Minimum49.086
Maximum2687379
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.3 KiB
2023-05-08T00:17:34.988968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum49.086
5-th percentile1589.575
Q15876.451
median15647.066
Q340319.5
95-th percentile221765.9
Maximum2687379
Range2687329.9
Interquartile range (IQR)34443.049

Descriptive statistics

Standard deviation209674.28
Coefficient of variation (CV)3.34764
Kurtosis76.883658
Mean62633.461
Median Absolute Deviation (MAD)11797.534
Skewness8.1601373
Sum3.0433599 × 108
Variance4.3963304 × 1010
MonotonicityNot monotonic
2023-05-08T00:17:35.090697image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20497 2
 
< 0.1%
18174 2
 
< 0.1%
34309 2
 
< 0.1%
20386 2
 
< 0.1%
6184 2
 
< 0.1%
13459 2
 
< 0.1%
20038 2
 
< 0.1%
6262 2
 
< 0.1%
45058 2
 
< 0.1%
121347 2
 
< 0.1%
Other values (4823) 4839
99.1%
(Missing) 23
 
0.5%
ValueCountFrequency (%)
49.086 1
< 0.1%
51.369 1
< 0.1%
59.504 1
< 0.1%
74.998 1
< 0.1%
77.164 1
< 0.1%
106 1
< 0.1%
106.159 1
< 0.1%
106.242 1
< 0.1%
108.746 1
< 0.1%
116.669 1
< 0.1%
ValueCountFrequency (%)
2687379 1
< 0.1%
2622532 1
< 0.1%
2573126 1
< 0.1%
2533600 1
< 0.1%
2490972 1
< 0.1%
2434079 1
< 0.1%
2415689 1
< 0.1%
2359141 1
< 0.1%
2354507 1
< 0.1%
2351698 1
< 0.1%

bkvlps
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct4795
Distinct (%)99.6%
Missing70
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean7929.4908
Minimum-125.6877
Maximum16297416
Zeros0
Zeros (%)0.0%
Negative182
Negative (%)3.7%
Memory size38.3 KiB
2023-05-08T00:17:35.190956image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-125.6877
5-th percentile1.519805
Q19.953625
median18.384
Q332.573475
95-th percentile72.674025
Maximum16297416
Range16297542
Interquartile range (IQR)22.61985

Descriptive statistics

Standard deviation327827.78
Coefficient of variation (CV)41.342855
Kurtosis2288.1667
Mean7929.4908
Median Absolute Deviation (MAD)10.1635
Skewness47.362372
Sum38156710
Variance1.0747106 × 1011
MonotonicityNot monotonic
2023-05-08T00:17:35.291643image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.3468 2
 
< 0.1%
10.3063 2
 
< 0.1%
12.9776 2
 
< 0.1%
5.9736 2
 
< 0.1%
5.4405 2
 
< 0.1%
7.7707 2
 
< 0.1%
60.1364 2
 
< 0.1%
21.4568 2
 
< 0.1%
19.8094 2
 
< 0.1%
21.4275 2
 
< 0.1%
Other values (4785) 4792
98.2%
(Missing) 70
 
1.4%
ValueCountFrequency (%)
-125.6877 1
< 0.1%
-87.732 1
< 0.1%
-74.1842 1
< 0.1%
-71.2976 1
< 0.1%
-70.1814 1
< 0.1%
-63.7648 1
< 0.1%
-61.3894 1
< 0.1%
-59.0613 1
< 0.1%
-56.826 1
< 0.1%
-55.494 1
< 0.1%
ValueCountFrequency (%)
16297416 1
< 0.1%
15437843 1
< 0.1%
2455471 1
< 0.1%
1956293 1
< 0.1%
1885901 1
< 0.1%
644.4382 1
< 0.1%
505.4673 1
< 0.1%
434.9748 1
< 0.1%
353.2199 1
< 0.1%
325.8986 1
< 0.1%

capx
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3888
Distinct (%)80.2%
Missing33
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean1241.3741
Minimum0
Maximum37985
Zeros309
Zeros (%)6.3%
Negative0
Negative (%)0.0%
Memory size38.3 KiB
2023-05-08T00:17:35.398477image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q183.435
median280.873
Q31113
95-th percentile5164.8
Maximum37985
Range37985
Interquartile range (IQR)1029.565

Descriptive statistics

Standard deviation2924.7561
Coefficient of variation (CV)2.3560635
Kurtosis44.020255
Mean1241.3741
Median Absolute Deviation (MAD)255.191
Skewness5.7506115
Sum6019422.9
Variance8554198.5
MonotonicityNot monotonic
2023-05-08T00:17:35.501933image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 309
 
6.3%
145 7
 
0.1%
121 7
 
0.1%
593 7
 
0.1%
180 7
 
0.1%
119 7
 
0.1%
162 7
 
0.1%
246 6
 
0.1%
224 5
 
0.1%
422 5
 
0.1%
Other values (3878) 4482
91.8%
(Missing) 33
 
0.7%
ValueCountFrequency (%)
0 309
6.3%
0.42 1
 
< 0.1%
0.899 1
 
< 0.1%
1.384 1
 
< 0.1%
1.506 1
 
< 0.1%
1.539 1
 
< 0.1%
1.584 1
 
< 0.1%
1.945 1
 
< 0.1%
2.031 1
 
< 0.1%
2.2 1
 
< 0.1%
ValueCountFrequency (%)
37985 1
< 0.1%
35407 1
< 0.1%
34271 1
< 0.1%
33669 1
< 0.1%
32952 1
< 0.1%
30975 1
< 0.1%
30938 1
< 0.1%
29504 1
< 0.1%
29166 1
< 0.1%
27633 1
< 0.1%

ceql
Real number (ℝ)

Distinct4785
Distinct (%)98.5%
Missing24
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean13133.283
Minimum-13244
Maximum424791
Zeros0
Zeros (%)0.0%
Negative182
Negative (%)3.7%
Memory size38.3 KiB
2023-05-08T00:17:35.607057image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-13244
5-th percentile187.0476
Q11955.957
median4995.85
Q311724.797
95-th percentile51662.85
Maximum424791
Range438035
Interquartile range (IQR)9768.84

Descriptive statistics

Standard deviation28367.252
Coefficient of variation (CV)2.1599514
Kurtosis41.736409
Mean13133.283
Median Absolute Deviation (MAD)3681.3425
Skewness5.6140678
Sum63801487
Variance8.0470099 × 108
MonotonicityNot monotonic
2023-05-08T00:17:35.701078image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23771 2
 
< 0.1%
5488 2
 
< 0.1%
4752 2
 
< 0.1%
1145 2
 
< 0.1%
-60 2
 
< 0.1%
-102 2
 
< 0.1%
1454 2
 
< 0.1%
7336 2
 
< 0.1%
8380 2
 
< 0.1%
1045.5 2
 
< 0.1%
Other values (4775) 4838
99.1%
(Missing) 24
 
0.5%
ValueCountFrequency (%)
-13244 1
< 0.1%
-12688 1
< 0.1%
-12629 1
< 0.1%
-12459 1
< 0.1%
-12086 1
< 0.1%
-11926 1
< 0.1%
-11577 1
< 0.1%
-9660 1
< 0.1%
-8617 1
< 0.1%
-8446 1
< 0.1%
ValueCountFrequency (%)
424791 1
< 0.1%
348703 1
< 0.1%
348296 1
< 0.1%
283001 1
< 0.1%
255550 1
< 0.1%
244823 1
< 0.1%
242999 1
< 0.1%
241620 1
< 0.1%
241409 1
< 0.1%
240170 1
< 0.1%

csho
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct4641
Distinct (%)96.4%
Missing69
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean597.31446
Minimum0.001
Maximum29058.361
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.3 KiB
2023-05-08T00:17:35.796814image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile47.3654
Q1129.954
median281
Q3561
95-th percentile2098.8932
Maximum29058.361
Range29058.36
Interquartile range (IQR)431.046

Descriptive statistics

Standard deviation1161.9103
Coefficient of variation (CV)1.9452238
Kurtosis98.837772
Mean597.31446
Median Absolute Deviation (MAD)174
Skewness7.1858145
Sum2874874.5
Variance1350035.5
MonotonicityNot monotonic
2023-05-08T00:17:35.892698image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
325.811 9
 
0.2%
242.6 7
 
0.1%
0.001 5
 
0.1%
333 4
 
0.1%
321 4
 
0.1%
418 3
 
0.1%
147 3
 
0.1%
776 3
 
0.1%
585 3
 
0.1%
967 3
 
0.1%
Other values (4631) 4769
97.7%
(Missing) 69
 
1.4%
ValueCountFrequency (%)
0.001 5
0.1%
0.271 2
 
< 0.1%
0.843 1
 
< 0.1%
1.011 1
 
< 0.1%
1.013 1
 
< 0.1%
1.015 1
 
< 0.1%
1.698 1
 
< 0.1%
2.782 1
 
< 0.1%
2.81 1
 
< 0.1%
3.578 1
 
< 0.1%
ValueCountFrequency (%)
29058.361 1
< 0.1%
10778.264 1
< 0.1%
10615.376 1
< 0.1%
10591.808 1
< 0.1%
10573.017 1
< 0.1%
10535.938 1
< 0.1%
10516.542 1
< 0.1%
10405.625 1
< 0.1%
10380.265 1
< 0.1%
10287.302 1
< 0.1%

dltt
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct4407
Distinct (%)91.0%
Missing40
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean10426.34
Minimum0
Maximum359180
Zeros258
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size38.3 KiB
2023-05-08T00:17:35.992381image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11108.1715
median3662.1
Q39365.4837
95-th percentile33532.5
Maximum359180
Range359180
Interquartile range (IQR)8257.3122

Descriptive statistics

Standard deviation26635.715
Coefficient of variation (CV)2.5546562
Kurtosis53.936952
Mean10426.34
Median Absolute Deviation (MAD)3057.862
Skewness6.7206404
Sum50484339
Variance7.0946131 × 108
MonotonicityNot monotonic
2023-05-08T00:17:36.089945image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 258
 
5.3%
250 13
 
0.3%
500 8
 
0.2%
150 7
 
0.1%
499 4
 
0.1%
799 3
 
0.1%
3935 3
 
0.1%
575 3
 
0.1%
750 3
 
0.1%
1099 3
 
0.1%
Other values (4397) 4537
92.9%
(Missing) 40
 
0.8%
ValueCountFrequency (%)
0 258
5.3%
0.018 1
 
< 0.1%
0.022 1
 
< 0.1%
0.038 1
 
< 0.1%
0.193 1
 
< 0.1%
0.349 1
 
< 0.1%
0.457 1
 
< 0.1%
0.485 1
 
< 0.1%
0.509 1
 
< 0.1%
0.648 1
 
< 0.1%
ValueCountFrequency (%)
359180 1
< 0.1%
324782 1
< 0.1%
309710 1
< 0.1%
279618 1
< 0.1%
274873 1
< 0.1%
274850 1
< 0.1%
271245 1
< 0.1%
268676 1
< 0.1%
268522 1
< 0.1%
266303 1
< 0.1%

dt
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct3959
Distinct (%)91.3%
Missing548
Missing (%)11.2%
Infinite0
Infinite (%)0.0%
Mean9866.0364
Minimum0
Maximum426314
Zeros237
Zeros (%)4.9%
Negative0
Negative (%)0.0%
Memory size38.3 KiB
2023-05-08T00:17:36.195452image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11033.7673
median3395.356
Q38691.365
95-th percentile32392
Maximum426314
Range426314
Interquartile range (IQR)7657.5977

Descriptive statistics

Standard deviation26541.057
Coefficient of variation (CV)2.6901438
Kurtosis82.503456
Mean9866.0364
Median Absolute Deviation (MAD)2840.5
Skewness8.0090617
Sum42759402
Variance7.044277 × 108
MonotonicityNot monotonic
2023-05-08T00:17:36.290626image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 237
 
4.9%
250 9
 
0.2%
500 5
 
0.1%
150 5
 
0.1%
799 3
 
0.1%
827 3
 
0.1%
35 3
 
0.1%
1199 3
 
0.1%
12825 3
 
0.1%
6838 3
 
0.1%
Other values (3949) 4060
83.2%
(Missing) 548
 
11.2%
ValueCountFrequency (%)
0 237
4.9%
0.009 1
 
< 0.1%
0.033 1
 
< 0.1%
0.056 1
 
< 0.1%
0.2 1
 
< 0.1%
0.274 1
 
< 0.1%
0.338 1
 
< 0.1%
0.349 1
 
< 0.1%
0.509 1
 
< 0.1%
0.525 1
 
< 0.1%
ValueCountFrequency (%)
426314 1
< 0.1%
398523 1
< 0.1%
381183 1
< 0.1%
362000 1
< 0.1%
344435 1
< 0.1%
333248 1
< 0.1%
323505 1
< 0.1%
273058 1
< 0.1%
272565 1
< 0.1%
259374 1
< 0.1%

dvp
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct411
Distinct (%)8.5%
Missing25
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean22.204622
Minimum-112
Maximum2018
Zeros4213
Zeros (%)86.3%
Negative2
Negative (%)< 0.1%
Memory size38.3 KiB
2023-05-08T00:17:36.391128image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-112
5-th percentile0
Q10
median0
Q30
95-th percentile73
Maximum2018
Range2130
Interquartile range (IQR)0

Descriptive statistics

Standard deviation134.4147
Coefficient of variation (CV)6.053456
Kurtosis99.483079
Mean22.204622
Median Absolute Deviation (MAD)0
Skewness9.4181971
Sum107847.85
Variance18067.311
MonotonicityNot monotonic
2023-05-08T00:17:36.483399image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4213
86.3%
1 20
 
0.4%
2 12
 
0.2%
14 12
 
0.2%
3.337 9
 
0.2%
340 8
 
0.2%
0.541 7
 
0.1%
10.5 6
 
0.1%
3 6
 
0.1%
10.2 6
 
0.1%
Other values (401) 558
 
11.4%
(Missing) 25
 
0.5%
ValueCountFrequency (%)
-112 1
 
< 0.1%
-29 1
 
< 0.1%
0 4213
86.3%
0.008 1
 
< 0.1%
0.009 2
 
< 0.1%
0.012 2
 
< 0.1%
0.019 1
 
< 0.1%
0.03 5
 
0.1%
0.153 1
 
< 0.1%
0.224 2
 
< 0.1%
ValueCountFrequency (%)
2018 1
< 0.1%
1932 1
< 0.1%
1704 1
< 0.1%
1682 1
< 0.1%
1663 1
< 0.1%
1647 1
< 0.1%
1629 1
< 0.1%
1614 1
< 0.1%
1611 1
< 0.1%
1587 1
< 0.1%

ebitda
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct4410
Distinct (%)95.5%
Missing264
Missing (%)5.4%
Infinite0
Infinite (%)0.0%
Mean4396.5382
Minimum-21913
Maximum115545
Zeros0
Zeros (%)0.0%
Negative62
Negative (%)1.3%
Memory size38.3 KiB
2023-05-08T00:17:36.578381image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-21913
5-th percentile203.3093
Q1811
median1842.65
Q34303.875
95-th percentile17405.75
Maximum115545
Range137458
Interquartile range (IQR)3492.875

Descriptive statistics

Standard deviation8160.3083
Coefficient of variation (CV)1.8560758
Kurtosis29.223214
Mean4396.5382
Median Absolute Deviation (MAD)1280.1
Skewness4.6450564
Sum20303213
Variance66590632
MonotonicityNot monotonic
2023-05-08T00:17:36.672979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4854 4
 
0.1%
2108 4
 
0.1%
811 3
 
0.1%
2113 3
 
0.1%
1684 3
 
0.1%
1088 3
 
0.1%
1221 3
 
0.1%
809 3
 
0.1%
2997 3
 
0.1%
3319 3
 
0.1%
Other values (4400) 4586
93.9%
(Missing) 264
 
5.4%
ValueCountFrequency (%)
-21913 1
< 0.1%
-15891 1
< 0.1%
-9647 1
< 0.1%
-4171 1
< 0.1%
-3124 1
< 0.1%
-2185 1
< 0.1%
-2007 1
< 0.1%
-1792 1
< 0.1%
-927 1
< 0.1%
-666.676 1
< 0.1%
ValueCountFrequency (%)
115545 1
< 0.1%
81730 1
< 0.1%
79962 1
< 0.1%
75230 1
< 0.1%
71565 1
< 0.1%
70744 1
< 0.1%
69687 1
< 0.1%
69276 1
< 0.1%
67089 1
< 0.1%
65769 1
< 0.1%

emp
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct2853
Distinct (%)59.3%
Missing71
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean51.071399
Minimum0.052
Maximum2300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.3 KiB
2023-05-08T00:17:36.774079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.052
5-th percentile1.215
Q17
median17
Q350
95-th percentile223.3575
Maximum2300
Range2299.948
Interquartile range (IQR)43

Descriptive statistics

Standard deviation128.09718
Coefficient of variation (CV)2.5081979
Kurtosis184.29779
Mean51.071399
Median Absolute Deviation (MAD)13.286
Skewness11.594522
Sum245704.5
Variance16408.886
MonotonicityNot monotonic
2023-05-08T00:17:36.872793image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 24
 
0.5%
30 23
 
0.5%
5 22
 
0.5%
14 22
 
0.5%
25 19
 
0.4%
24 19
 
0.4%
50 18
 
0.4%
22 18
 
0.4%
7 17
 
0.3%
35 17
 
0.3%
Other values (2843) 4612
94.5%
(Missing) 71
 
1.5%
ValueCountFrequency (%)
0.052 1
 
< 0.1%
0.068 1
 
< 0.1%
0.079 1
 
< 0.1%
0.083 1
 
< 0.1%
0.097 1
 
< 0.1%
0.114 1
 
< 0.1%
0.116 1
 
< 0.1%
0.125 1
 
< 0.1%
0.132 1
 
< 0.1%
0.14 3
0.1%
ValueCountFrequency (%)
2300 3
0.1%
2200 6
0.1%
2100 2
 
< 0.1%
798 1
 
< 0.1%
647.5 1
 
< 0.1%
566 1
 
< 0.1%
539 1
 
< 0.1%
537 1
 
< 0.1%
523 1
 
< 0.1%
505 1
 
< 0.1%

lct
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3904
Distinct (%)97.1%
Missing860
Missing (%)17.6%
Infinite0
Infinite (%)0.0%
Mean6214.9261
Minimum13.203
Maximum116866
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.3 KiB
2023-05-08T00:17:36.971681image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum13.203
5-th percentile238.62265
Q1933.992
median2418.283
Q36052.5
95-th percentile26616.55
Maximum116866
Range116852.8
Interquartile range (IQR)5118.508

Descriptive statistics

Standard deviation11160.336
Coefficient of variation (CV)1.7957311
Kurtosis22.712287
Mean6214.9261
Median Absolute Deviation (MAD)1812.6045
Skewness4.2022502
Sum24996433
Variance1.245531 × 108
MonotonicityNot monotonic
2023-05-08T00:17:37.070185image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5374 3
 
0.1%
2467 3
 
0.1%
3485 3
 
0.1%
12708 3
 
0.1%
1501 3
 
0.1%
2747 3
 
0.1%
2611 3
 
0.1%
2265 3
 
0.1%
3577 2
 
< 0.1%
3385 2
 
< 0.1%
Other values (3894) 3994
81.8%
(Missing) 860
 
17.6%
ValueCountFrequency (%)
13.203 1
< 0.1%
14.741 1
< 0.1%
15.129 1
< 0.1%
17.024 1
< 0.1%
18.188 1
< 0.1%
19.219 1
< 0.1%
19.5 1
< 0.1%
20.01 1
< 0.1%
23.46 1
< 0.1%
26.07 1
< 0.1%
ValueCountFrequency (%)
116866 1
< 0.1%
105718 1
< 0.1%
100814 1
< 0.1%
98132 1
< 0.1%
97312 1
< 0.1%
95569 1
< 0.1%
94600 1
< 0.1%
90281 1
< 0.1%
87812 1
< 0.1%
85181 1
< 0.1%

lt
Real number (ℝ)

Distinct4805
Distinct (%)99.2%
Missing37
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean48936.046
Minimum15.985
Maximum2426049
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.3 KiB
2023-05-08T00:17:37.170446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum15.985
5-th percentile597.4024
Q13348
median9683.453
Q327430
95-th percentile165172.6
Maximum2426049
Range2426033
Interquartile range (IQR)24082

Descriptive statistics

Standard deviation186723.4
Coefficient of variation (CV)3.8156618
Kurtosis81.014572
Mean48936.046
Median Absolute Deviation (MAD)7774.494
Skewness8.4483769
Sum2.3709514 × 108
Variance3.4865628 × 1010
MonotonicityNot monotonic
2023-05-08T00:17:37.273576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13794 2
 
< 0.1%
34596 2
 
< 0.1%
3492 2
 
< 0.1%
47751 2
 
< 0.1%
2997.8 2
 
< 0.1%
16374 2
 
< 0.1%
56737 2
 
< 0.1%
14037 2
 
< 0.1%
14229 2
 
< 0.1%
6779 2
 
< 0.1%
Other values (4795) 4825
98.8%
(Missing) 37
 
0.8%
ValueCountFrequency (%)
15.985 1
< 0.1%
16.171 1
< 0.1%
17.024 1
< 0.1%
21.023 1
< 0.1%
21.944 1
< 0.1%
28.868 1
< 0.1%
29 1
< 0.1%
30.99 1
< 0.1%
31.548 1
< 0.1%
34.708 1
< 0.1%
ValueCountFrequency (%)
2426049 1
< 0.1%
2366017 1
< 0.1%
2341061 1
< 0.1%
2277907 1
< 0.1%
2236782 1
< 0.1%
2204511 1
< 0.1%
2169269 1
< 0.1%
2155072 1
< 0.1%
2104125 1
< 0.1%
2089182 1
< 0.1%

ni
Real number (ℝ)

Distinct4477
Distinct (%)92.2%
Missing25
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean1920.1609
Minimum-23119
Maximum81417
Zeros0
Zeros (%)0.0%
Negative342
Negative (%)7.0%
Memory size38.3 KiB
2023-05-08T00:17:37.380037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-23119
5-th percentile-97.7168
Q1286.198
median704.422
Q31889
95-th percentile8169.8
Maximum81417
Range104536
Interquartile range (IQR)1602.802

Descriptive statistics

Standard deviation4385.3296
Coefficient of variation (CV)2.2838344
Kurtosis59.008623
Mean1920.1609
Median Absolute Deviation (MAD)544.295
Skewness5.8889373
Sum9326221.7
Variance19231116
MonotonicityNot monotonic
2023-05-08T00:17:37.486002image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
263 4
 
0.1%
753 4
 
0.1%
591 4
 
0.1%
477 4
 
0.1%
1979 4
 
0.1%
858 4
 
0.1%
470 3
 
0.1%
242 3
 
0.1%
817 3
 
0.1%
563 3
 
0.1%
Other values (4467) 4821
98.8%
(Missing) 25
 
0.5%
ValueCountFrequency (%)
-23119 1
< 0.1%
-22355 1
< 0.1%
-14454 1
< 0.1%
-12650 1
< 0.1%
-12236 1
< 0.1%
-10192 1
< 0.1%
-10137 1
< 0.1%
-7829 1
< 0.1%
-7642 1
< 0.1%
-6917.9 1
< 0.1%
ValueCountFrequency (%)
81417 1
< 0.1%
59531 1
< 0.1%
55256 1
< 0.1%
53394 1
< 0.1%
48351 1
< 0.1%
45687 1
< 0.1%
44940 1
< 0.1%
44880 1
< 0.1%
41733 1
< 0.1%
41060 1
< 0.1%

ppegt
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct4278
Distinct (%)98.1%
Missing522
Missing (%)10.7%
Infinite0
Infinite (%)0.0%
Mean16275.383
Minimum0
Maximum493335
Zeros9
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size38.3 KiB
2023-05-08T00:17:37.585649image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile207.6715
Q11150
median3925
Q316270.75
95-th percentile61051.5
Maximum493335
Range493335
Interquartile range (IQR)15120.75

Descriptive statistics

Standard deviation38028.927
Coefficient of variation (CV)2.3365919
Kurtosis55.465276
Mean16275.383
Median Absolute Deviation (MAD)3406.88
Skewness6.4634165
Sum70960670
Variance1.4461993 × 109
MonotonicityNot monotonic
2023-05-08T00:17:37.691965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9
 
0.2%
866 3
 
0.1%
1733 3
 
0.1%
1063 3
 
0.1%
1054 3
 
0.1%
2500 3
 
0.1%
2000 3
 
0.1%
858 2
 
< 0.1%
33686 2
 
< 0.1%
2139.3 2
 
< 0.1%
Other values (4268) 4327
88.6%
(Missing) 522
 
10.7%
ValueCountFrequency (%)
0 9
0.2%
2.348 1
 
< 0.1%
8.81 1
 
< 0.1%
9.3 1
 
< 0.1%
11.57 1
 
< 0.1%
12.146 1
 
< 0.1%
12.297 1
 
< 0.1%
13.552 1
 
< 0.1%
15.09 1
 
< 0.1%
21.774 1
 
< 0.1%
ValueCountFrequency (%)
493335 1
< 0.1%
477190 1
< 0.1%
477185 1
< 0.1%
453915 1
< 0.1%
447337 1
< 0.1%
446789 1
< 0.1%
434517 1
< 0.1%
409314 1
< 0.1%
393995 1
< 0.1%
373938 1
< 0.1%

pstkl
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct390
Distinct (%)8.0%
Missing32
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean317.7737
Minimum0
Maximum49148
Zeros4147
Zeros (%)84.9%
Negative0
Negative (%)0.0%
Memory size38.3 KiB
2023-05-08T00:17:37.796528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1041.85
Maximum49148
Range49148
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2022.1028
Coefficient of variation (CV)6.3633424
Kurtosis154.19309
Mean317.7737
Median Absolute Deviation (MAD)0
Skewness11.024392
Sum1541202.4
Variance4088899.9
MonotonicityNot monotonic
2023-05-08T00:17:37.901953image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4147
84.9%
200 15
 
0.3%
325 11
 
0.2%
30.4 10
 
0.2%
39.847 10
 
0.2%
0.3 10
 
0.2%
142 10
 
0.2%
575 9
 
0.2%
155.6 8
 
0.2%
1000 8
 
0.2%
Other values (380) 612
 
12.5%
(Missing) 32
 
0.7%
ValueCountFrequency (%)
0 4147
84.9%
0.015 3
 
0.1%
0.057 4
 
0.1%
0.058 2
 
< 0.1%
0.061 1
 
< 0.1%
0.069 1
 
< 0.1%
0.07 1
 
< 0.1%
0.236 1
 
< 0.1%
0.3 10
 
0.2%
0.551 1
 
< 0.1%
ValueCountFrequency (%)
49148 1
 
< 0.1%
26993 1
 
< 0.1%
26068 4
0.1%
25969.47 1
 
< 0.1%
25659.47 1
 
< 0.1%
25220 1
 
< 0.1%
23819.47 1
 
< 0.1%
23401 1
 
< 0.1%
23359.47 1
 
< 0.1%
22326 1
 
< 0.1%

revt
Real number (ℝ)

Distinct4807
Distinct (%)99.0%
Missing25
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean21176.691
Minimum27.253
Maximum521426
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.3 KiB
2023-05-08T00:17:38.005151image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum27.253
5-th percentile923.8702
Q13326.445
median8073.855
Q318031
95-th percentile93525.6
Maximum521426
Range521398.75
Interquartile range (IQR)14704.555

Descriptive statistics

Standard deviation41346.83
Coefficient of variation (CV)1.9524688
Kurtosis44.33917
Mean21176.691
Median Absolute Deviation (MAD)5722.145
Skewness5.4985157
Sum1.0285519 × 108
Variance1.7095604 × 109
MonotonicityNot monotonic
2023-05-08T00:17:38.113133image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7059 2
 
< 0.1%
5539 2
 
< 0.1%
11454 2
 
< 0.1%
5763 2
 
< 0.1%
14945 2
 
< 0.1%
62484 2
 
< 0.1%
48158 2
 
< 0.1%
8521 2
 
< 0.1%
9968 2
 
< 0.1%
5010 2
 
< 0.1%
Other values (4797) 4837
99.1%
(Missing) 25
 
0.5%
ValueCountFrequency (%)
27.253 1
< 0.1%
43.329 1
< 0.1%
48.631 1
< 0.1%
48.672 1
< 0.1%
60.209 1
< 0.1%
61.661 1
< 0.1%
74.962 1
< 0.1%
76.266 1
< 0.1%
76.81 1
< 0.1%
79.035 1
< 0.1%
ValueCountFrequency (%)
521426 1
< 0.1%
511729 1
< 0.1%
496785 1
< 0.1%
483521 1
< 0.1%
482154 1
< 0.1%
479962 1
< 0.1%
474259 1
< 0.1%
467231 1
< 0.1%
444948 1
< 0.1%
433526 1
< 0.1%

seq
Real number (ℝ)

Distinct4784
Distinct (%)98.5%
Missing23
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean13441.878
Minimum-13244
Maximum424791
Zeros0
Zeros (%)0.0%
Negative178
Negative (%)3.6%
Memory size38.3 KiB
2023-05-08T00:17:38.222744image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-13244
5-th percentile188.5761
Q11961.916
median5047.208
Q311876.4
95-th percentile52065.7
Maximum424791
Range438035
Interquartile range (IQR)9914.484

Descriptive statistics

Standard deviation29471.794
Coefficient of variation (CV)2.1925355
Kurtosis41.673893
Mean13441.878
Median Absolute Deviation (MAD)3739.008
Skewness5.6761767
Sum65314084
Variance8.6858667 × 108
MonotonicityNot monotonic
2023-05-08T00:17:38.323057image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10381 3
 
0.1%
2162 3
 
0.1%
-102 2
 
< 0.1%
7336 2
 
< 0.1%
5115 2
 
< 0.1%
6320 2
 
< 0.1%
2291 2
 
< 0.1%
3135 2
 
< 0.1%
11274 2
 
< 0.1%
2127 2
 
< 0.1%
Other values (4774) 4837
99.1%
(Missing) 23
 
0.5%
ValueCountFrequency (%)
-13244 1
< 0.1%
-12688 1
< 0.1%
-12629 1
< 0.1%
-12459 1
< 0.1%
-12086 1
< 0.1%
-11785 1
< 0.1%
-11577 1
< 0.1%
-9660 1
< 0.1%
-8617 1
< 0.1%
-8446 1
< 0.1%
ValueCountFrequency (%)
424791 1
< 0.1%
348703 1
< 0.1%
348296 1
< 0.1%
283001 1
< 0.1%
267146 1
< 0.1%
266840 1
< 0.1%
265325 1
< 0.1%
264810 1
< 0.1%
261330 1
< 0.1%
256515 1
< 0.1%

teq
Real number (ℝ)

Distinct4782
Distinct (%)98.4%
Missing23
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean13782.31
Minimum-11476
Maximum428563
Zeros0
Zeros (%)0.0%
Negative162
Negative (%)3.3%
Memory size38.3 KiB
2023-05-08T00:17:39.323088image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-11476
5-th percentile238.8368
Q12001
median5215.5
Q312187
95-th percentile54004.8
Maximum428563
Range440039
Interquartile range (IQR)10186

Descriptive statistics

Standard deviation29886.996
Coefficient of variation (CV)2.1685041
Kurtosis40.550356
Mean13782.31
Median Absolute Deviation (MAD)3869.331
Skewness5.5943057
Sum66968245
Variance8.9323256 × 108
MonotonicityNot monotonic
2023-05-08T00:17:39.451750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8063 3
 
0.1%
11296 2
 
< 0.1%
2622.9 2
 
< 0.1%
4714 2
 
< 0.1%
7088 2
 
< 0.1%
7235 2
 
< 0.1%
2075 2
 
< 0.1%
2541 2
 
< 0.1%
243 2
 
< 0.1%
7679 2
 
< 0.1%
Other values (4772) 4838
99.1%
(Missing) 23
 
0.5%
ValueCountFrequency (%)
-11476 1
< 0.1%
-11203 1
< 0.1%
-10900 1
< 0.1%
-10739 1
< 0.1%
-10653 1
< 0.1%
-10230 1
< 0.1%
-9599 1
< 0.1%
-8446 1
< 0.1%
-8341 1
< 0.1%
-8300 1
< 0.1%
ValueCountFrequency (%)
428563 1
< 0.1%
352500 1
< 0.1%
351954 1
< 0.1%
286359 1
< 0.1%
267146 1
< 0.1%
266840 1
< 0.1%
265325 1
< 0.1%
264810 1
< 0.1%
261330 1
< 0.1%
258627 1
< 0.1%

xad
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1524
Distinct (%)70.5%
Missing2720
Missing (%)55.7%
Infinite0
Infinite (%)0.0%
Mean571.4789
Minimum0
Maximum11000
Zeros7
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size38.3 KiB
2023-05-08T00:17:39.551949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.205
Q137.2
median153
Q3522.61975
95-th percentile2799.9
Maximum11000
Range11000
Interquartile range (IQR)485.41975

Descriptive statistics

Standard deviation1115.1669
Coefficient of variation (CV)1.9513701
Kurtosis20.370478
Mean571.4789
Median Absolute Deviation (MAD)140.2
Skewness3.9220123
Sum1235537.4
Variance1243597.1
MonotonicityNot monotonic
2023-05-08T00:17:39.654557image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2600 10
 
0.2%
2500 10
 
0.2%
1200 10
 
0.2%
1600 9
 
0.2%
30 8
 
0.2%
2400 8
 
0.2%
98 8
 
0.2%
11 7
 
0.1%
1000 7
 
0.1%
1100 7
 
0.1%
Other values (1514) 2078
42.6%
(Missing) 2720
55.7%
ValueCountFrequency (%)
0 7
0.1%
0.1 1
 
< 0.1%
0.2 1
 
< 0.1%
0.3 2
 
< 0.1%
0.4 2
 
< 0.1%
0.5 2
 
< 0.1%
0.6 1
 
< 0.1%
0.8 1
 
< 0.1%
1 1
 
< 0.1%
1.1 3
0.1%
ValueCountFrequency (%)
11000 1
< 0.1%
9729 1
< 0.1%
9345 1
< 0.1%
9315 1
< 0.1%
9236 1
< 0.1%
8576 1
< 0.1%
8290 1
< 0.1%
8200 1
< 0.1%
7617 1
< 0.1%
7243 1
< 0.1%

xrd
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct1919
Distinct (%)67.8%
Missing2053
Missing (%)42.1%
Infinite0
Infinite (%)0.0%
Mean888.34346
Minimum0
Maximum35931
Zeros651
Zeros (%)13.3%
Negative0
Negative (%)0.0%
Memory size38.3 KiB
2023-05-08T00:17:39.755928image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q116.476
median148.258
Q3651
95-th percentile5151
Maximum35931
Range35931
Interquartile range (IQR)634.524

Descriptive statistics

Standard deviation2250.3446
Coefficient of variation (CV)2.5331921
Kurtosis49.796168
Mean888.34346
Median Absolute Deviation (MAD)148.258
Skewness5.7194273
Sum2513123.7
Variance5064050.9
MonotonicityNot monotonic
2023-05-08T00:17:39.852443image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 651
 
13.3%
1200 5
 
0.1%
24 5
 
0.1%
376 5
 
0.1%
96 5
 
0.1%
56 5
 
0.1%
647 4
 
0.1%
1300 4
 
0.1%
93 4
 
0.1%
33 4
 
0.1%
Other values (1909) 2137
43.8%
(Missing) 2053
42.1%
ValueCountFrequency (%)
0 651
13.3%
0.153 1
 
< 0.1%
1.109 1
 
< 0.1%
1.582 1
 
< 0.1%
1.632 1
 
< 0.1%
2.146 1
 
< 0.1%
3.198 1
 
< 0.1%
3.3 1
 
< 0.1%
3.955 1
 
< 0.1%
4.325 1
 
< 0.1%
ValueCountFrequency (%)
35931 1
< 0.1%
28837 1
< 0.1%
26018 1
< 0.1%
22620 1
< 0.1%
21419 1
< 0.1%
16876 1
< 0.1%
16625 1
< 0.1%
16217 1
< 0.1%
16085 1
< 0.1%
14726 1
< 0.1%

cik
Real number (ℝ)

Distinct498
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean767463.36
Minimum1800
Maximum1932393
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.3 KiB
2023-05-08T00:17:39.960259image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1800
5-th percentile16741.3
Q196021
median878927
Q31120193
95-th percentile1613103
Maximum1932393
Range1930593
Interquartile range (IQR)1024172

Descriptive statistics

Standard deviation539146.57
Coefficient of variation (CV)0.70250464
Kurtosis-1.1918842
Mean767463.36
Median Absolute Deviation (MAD)445477
Skewness-0.10135904
Sum3.7467561 × 109
Variance2.9067903 × 1011
MonotonicityNot monotonic
2023-05-08T00:17:40.064052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56873 11
 
0.2%
104169 11
 
0.2%
27419 11
 
0.2%
1123360 11
 
0.2%
745732 11
 
0.2%
1108524 11
 
0.2%
1285785 11
 
0.2%
1336920 11
 
0.2%
1045810 11
 
0.2%
764478 11
 
0.2%
Other values (488) 4772
97.7%
ValueCountFrequency (%)
1800 10
0.2%
2488 10
0.2%
2969 10
0.2%
4127 10
0.2%
4281 5
0.1%
4447 10
0.2%
4904 10
0.2%
4962 10
0.2%
4977 10
0.2%
5272 10
0.2%
ValueCountFrequency (%)
1932393 1
 
< 0.1%
1841666 10
0.2%
1821825 1
 
< 0.1%
1792044 10
0.2%
1783180 5
0.1%
1781335 5
0.1%
1757898 10
0.2%
1755672 5
0.1%
1754301 6
0.1%
1751788 5
0.1%

costat
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.3 KiB
A
4882 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4882
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 4882
100.0%

Length

2023-05-08T00:17:40.155227image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-08T00:17:40.220904image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
a 4882
100.0%

Most occurring characters

ValueCountFrequency (%)
A 4882
100.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4882
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 4882
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4882
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 4882
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4882
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 4882
100.0%

dvpsp_f
Real number (ℝ)

MISSING  ZEROS 

Distinct848
Distinct (%)17.7%
Missing93
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean1.2480741
Minimum0
Maximum46
Zeros1110
Zeros (%)22.7%
Negative0
Negative (%)0.0%
Memory size38.3 KiB
2023-05-08T00:17:40.291385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.08
median0.9
Q31.8
95-th percentile3.6
Maximum46
Range46
Interquartile range (IQR)1.72

Descriptive statistics

Standard deviation1.7444404
Coefficient of variation (CV)1.3977058
Kurtosis157.23123
Mean1.2480741
Median Absolute Deviation (MAD)0.84
Skewness8.6279011
Sum5977.0268
Variance3.0430724
MonotonicityNot monotonic
2023-05-08T00:17:40.390220image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1110
 
22.7%
1 57
 
1.2%
0.6 53
 
1.1%
0.2 51
 
1.0%
0.4 48
 
1.0%
0.8 44
 
0.9%
2 39
 
0.8%
1.6 39
 
0.8%
0.72 37
 
0.8%
0.04 36
 
0.7%
Other values (838) 3275
67.1%
(Missing) 93
 
1.9%
ValueCountFrequency (%)
0 1110
22.7%
0.018 2
 
< 0.1%
0.029 1
 
< 0.1%
0.03 2
 
< 0.1%
0.04 36
 
0.7%
0.0425 1
 
< 0.1%
0.0433 1
 
< 0.1%
0.06 13
 
0.3%
0.07 1
 
< 0.1%
0.075 2
 
< 0.1%
ValueCountFrequency (%)
46 1
< 0.1%
34.85 1
< 0.1%
30 1
< 0.1%
26.6134 1
< 0.1%
25 1
< 0.1%
18.5 1
< 0.1%
17.7051 1
< 0.1%
14.92 1
< 0.1%
13.2 1
< 0.1%
13.0638 1
< 0.1%

mkvalt
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct4478
Distinct (%)99.9%
Missing401
Missing (%)8.2%
Infinite0
Infinite (%)0.0%
Mean37117.961
Minimum62.8917
Maximum1073390.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.3 KiB
2023-05-08T00:17:40.504130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum62.8917
5-th percentile3291.9376
Q18341.6526
median15763.961
Q333633.639
95-th percentile151974.21
Maximum1073390.5
Range1073327.6
Interquartile range (IQR)25291.986

Descriptive statistics

Standard deviation70851.297
Coefficient of variation (CV)1.9088144
Kurtosis58.258834
Mean37117.961
Median Absolute Deviation (MAD)9371.0484
Skewness6.2412538
Sum1.6632558 × 108
Variance5.0199062 × 109
MonotonicityNot monotonic
2023-05-08T00:17:40.607565image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36812.696 2
 
< 0.1%
19664.7 2
 
< 0.1%
25119.22 2
 
< 0.1%
23357.3515 1
 
< 0.1%
29372.9636 1
 
< 0.1%
28619.1045 1
 
< 0.1%
35080.2063 1
 
< 0.1%
19144.2318 1
 
< 0.1%
52819.7318 1
 
< 0.1%
16902.5553 1
 
< 0.1%
Other values (4468) 4468
91.5%
(Missing) 401
 
8.2%
ValueCountFrequency (%)
62.8917 1
< 0.1%
117.3438 1
< 0.1%
149.1244 1
< 0.1%
160.8317 1
< 0.1%
188.0294 1
< 0.1%
207.0527 1
< 0.1%
266.5571 1
< 0.1%
267.0598 1
< 0.1%
506.2756 1
< 0.1%
509.7578 1
< 0.1%
ValueCountFrequency (%)
1073390.54 1
< 0.1%
1023856.28 1
< 0.1%
995151.5669 1
< 0.1%
921138.3192 1
< 0.1%
920224.32 1
< 0.1%
790050.0981 1
< 0.1%
757028.97 1
< 0.1%
737467.27 1
< 0.1%
729439.0252 1
< 0.1%
723558.7085 1
< 0.1%

prcc_f
Real number (ℝ)

Distinct4077
Distinct (%)85.4%
Missing106
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean89.483715
Minimum0.35
Maximum3808.41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.3 KiB
2023-05-08T00:17:40.711627image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.35
5-th percentile17.62
Q137.85
median61.205
Q396.2375
95-th percentile226.5575
Maximum3808.41
Range3808.06
Interquartile range (IQR)58.3875

Descriptive statistics

Standard deviation141.99435
Coefficient of variation (CV)1.5868177
Kurtosis222.33198
Mean89.483715
Median Absolute Deviation (MAD)26.83
Skewness11.871569
Sum427374.22
Variance20162.394
MonotonicityNot monotonic
2023-05-08T00:17:40.810726image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72.09 4
 
0.1%
36.11 4
 
0.1%
56.43 4
 
0.1%
19.54 4
 
0.1%
58.48 4
 
0.1%
68.56 4
 
0.1%
41.31 4
 
0.1%
76.61 4
 
0.1%
35.3 4
 
0.1%
37.74 4
 
0.1%
Other values (4067) 4736
97.0%
(Missing) 106
 
2.2%
ValueCountFrequency (%)
0.35 1
< 0.1%
0.795 1
< 0.1%
1.01 1
< 0.1%
2.4 1
< 0.1%
2.41 1
< 0.1%
2.67 1
< 0.1%
2.87 1
< 0.1%
3.51 1
< 0.1%
3.65 1
< 0.1%
3.87 1
< 0.1%
ValueCountFrequency (%)
3808.41 1
< 0.1%
3508.22 1
< 0.1%
2436.99 1
< 0.1%
2053.73 1
< 0.1%
1847.84 1
< 0.1%
1737.74 1
< 0.1%
1722.42 1
< 0.1%
1669 1
< 0.1%
1643 1
< 0.1%
1501.97 1
< 0.1%

gsector
Real number (ℝ)

Distinct11
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.353748
Minimum10
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.3 KiB
2023-05-08T00:17:40.893291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile15
Q120
median35
Q345
95-th percentile60
Maximum60
Range50
Interquartile range (IQR)25

Descriptive statistics

Standard deviation13.587538
Coefficient of variation (CV)0.39551836
Kurtosis-0.87781663
Mean34.353748
Median Absolute Deviation (MAD)10
Skewness0.09908931
Sum167715
Variance184.6212
MonotonicityNot monotonic
2023-05-08T00:17:40.964009image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
40 734
15.0%
20 724
14.8%
45 622
12.7%
35 613
12.6%
25 529
10.8%
30 363
7.4%
55 290
 
5.9%
60 289
 
5.9%
15 281
 
5.8%
10 226
 
4.6%
ValueCountFrequency (%)
10 226
 
4.6%
15 281
 
5.8%
20 724
14.8%
25 529
10.8%
30 363
7.4%
35 613
12.6%
40 734
15.0%
45 622
12.7%
50 211
 
4.3%
55 290
 
5.9%
ValueCountFrequency (%)
60 289
 
5.9%
55 290
 
5.9%
50 211
 
4.3%
45 622
12.7%
40 734
15.0%
35 613
12.6%
30 363
7.4%
25 529
10.8%
20 724
14.8%
15 281
 
5.8%

naics
Real number (ℝ)

Distinct206
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean385438.06
Minimum42
Maximum999977
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.3 KiB
2023-05-08T00:17:41.055495image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum42
5-th percentile2211
Q1325412
median339113
Q3522210
95-th percentile561710
Maximum999977
Range999935
Interquartile range (IQR)196798

Descriptive statistics

Standard deviation177729.88
Coefficient of variation (CV)0.46111141
Kurtosis0.52549435
Mean385438.06
Median Absolute Deviation (MAD)172097
Skewness-0.56581976
Sum1.8817086 × 109
Variance3.1587912 × 1010
MonotonicityNot monotonic
2023-05-08T00:17:41.159628image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
522110 188
 
3.9%
334413 168
 
3.4%
531120 153
 
3.1%
22111 140
 
2.9%
2211 120
 
2.5%
524126 110
 
2.3%
325412 109
 
2.2%
2111 100
 
2.0%
518210 96
 
2.0%
531110 86
 
1.8%
Other values (196) 3612
74.0%
ValueCountFrequency (%)
42 10
 
0.2%
111 5
 
0.1%
315 19
 
0.4%
321 10
 
0.2%
325 10
 
0.2%
423 10
 
0.2%
621 10
 
0.2%
2111 100
2.0%
2211 120
2.5%
3113 10
 
0.2%
ValueCountFrequency (%)
999977 20
0.4%
812331 10
 
0.2%
722513 40
0.8%
722511 20
0.4%
721120 30
0.6%
721110 20
0.4%
713210 6
 
0.1%
711320 10
 
0.2%
622110 20
0.4%
621511 20
0.4%

Interactions

2023-05-08T00:17:29.202252image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:07.938807image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:11.320840image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:13.995422image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:16.538290image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:19.426921image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:22.271321image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:25.093118image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:28.213342image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:30.917891image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:33.529380image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:36.588909image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:39.287608image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:41.918200image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:45.025401image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:47.582496image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:50.278867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:52.910990image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:56.016044image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:58.609786image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:01.217612image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:03.859817image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:07.038500image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:09.688792image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:12.311575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:15.086558image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:17.871719image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:20.604477image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:23.964913image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:26.634682image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:29.289909image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:08.034516image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:11.409308image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:14.077698image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:16.631230image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:19.513526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:22.366790image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:25.184907image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:28.298148image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:31.005490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:33.618748image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:36.675050image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:39.390197image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:42.008293image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:45.109198image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:47.673434image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:50.367967image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:52.997803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:56.102876image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:58.697900image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:01.306931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:03.944241image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:07.127835image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:09.770044image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:12.404084image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:15.181589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:17.960098image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:20.694446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:24.056276image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:26.722299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:29.373972image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:08.125368image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:11.489989image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:14.156956image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:16.723842image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:19.595236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:22.454761image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:25.270324image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:28.377238image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:31.090024image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:33.703613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:36.759842image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:39.504627image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:42.096791image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:45.191764image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:47.769231image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:50.451154image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:53.078648image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:56.186251image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:58.783622image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:01.395145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:04.025632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:07.208955image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:09.852136image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:12.489487image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:15.272137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:18.047414image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:20.778291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:24.141898image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:26.805864image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:29.459243image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:08.207976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-05-08T00:16:19.685939image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:22.542461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:25.361137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:28.461319image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:31.174631image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:33.794789image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:36.846908image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:39.604080image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:42.185491image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:45.273862image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:47.874044image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:50.536841image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:53.673339image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:56.271161image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:58.866977image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:01.479318image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:04.107523image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:07.293800image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:09.933050image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:12.578832image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:15.367754image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:18.138050image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:20.864617image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:24.229841image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:26.889012image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:29.554185image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:08.300632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:11.664307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-05-08T00:16:55.591830image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:58.172737image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:00.787214image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:03.405708image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:06.584971image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:09.231581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:11.887708image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:14.572763image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:17.396135image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:20.181968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:23.534009image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:26.193838image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:28.781712image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:31.566366image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:10.964755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:13.658275image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:16.186324image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:19.068616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:21.920421image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:24.672679image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:27.852679image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:30.572625image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:33.174504image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:36.214722image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:38.886093image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:41.580168image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:44.664826image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:47.235553image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:49.926866image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:52.566131image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:55.673525image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:58.255841image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:00.866739image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:03.492105image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:06.666923image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:09.311110image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:11.970051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:14.667446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:17.486942image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:20.261516image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:23.617990image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:26.278125image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:28.861498image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:31.656281image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:11.051928image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:13.742967image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:16.272078image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:19.159068image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:22.006249image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:24.772518image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:27.943337image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:30.667608image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:33.262486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:36.311203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:38.981654image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:41.663929image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:44.749857image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:47.319795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:50.012435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:52.650518image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:55.756598image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:58.347547image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:00.952516image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:03.596394image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:06.767358image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:09.399076image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:12.053203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:14.768030image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:17.579983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:20.347349image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:23.704079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:26.368460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:28.947431image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:31.749368image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:11.143177image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:13.830821image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:16.361897image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:19.242574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:22.100821image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:24.881745image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:28.036269image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:30.753990image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:33.352261image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:36.405135image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:39.088489image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:41.753232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:44.845654image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:47.409321image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:50.094814image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:52.739145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:55.846441image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:58.434632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:01.046205image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:03.689157image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:06.862859image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:09.512111image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:12.140040image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:14.881261image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:17.680426image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:20.438656image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:23.793873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:26.461727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:29.035405image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:31.835019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:11.230192image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:13.912207image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:16.451184image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:19.333174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:22.186278image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:24.991201image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:28.123930image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:30.837180image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:33.437304image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:36.496350image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:39.186917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:41.834417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:44.935980image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:47.495550image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:50.182957image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:52.822045image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:55.930169image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:16:58.521399image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:01.131341image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:03.771777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:06.948793image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:09.604108image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:12.222305image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:14.988650image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:17.776719image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:20.520871image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:23.878513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:26.545876image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-08T00:17:29.116673image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-05-08T00:17:41.278606image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
gvkeyfyearfyractatbkvlpscapxceqlcshodlttdtdvpebitdaemplctltnippegtpstklrevtseqteqxadxrdcikdvpsp_fmkvaltprcc_fgsectornaics
gvkey1.0000.0150.085-0.287-0.315-0.055-0.298-0.281-0.335-0.290-0.252-0.055-0.342-0.322-0.329-0.313-0.284-0.379-0.064-0.347-0.281-0.277-0.312-0.2260.666-0.254-0.2630.0840.1120.250
fyear0.0151.0000.0640.1210.1560.1290.0980.1180.0520.2110.2220.0180.1560.0670.1580.1600.1420.1120.0140.1140.1180.1190.1030.0740.0270.2120.2900.3600.000-0.000
fyr0.0850.0641.000-0.0640.1360.174-0.0230.080-0.0280.1770.1330.1710.046-0.148-0.0060.149-0.0220.0670.144-0.0580.0840.091-0.064-0.0000.0340.028-0.0040.0310.0150.060
act-0.2870.121-0.0641.0000.8260.1960.6130.7120.7390.5990.6450.0670.8140.6620.9000.7870.7070.5890.0600.8600.7110.7110.6540.525-0.1780.3400.7620.085-0.014-0.102
at-0.3150.1560.1360.8261.0000.3960.5670.8690.7480.8370.8400.3270.8810.5360.9030.9750.6950.7790.3010.7840.8700.8750.6210.401-0.1740.3630.7420.0030.078-0.008
bkvlps-0.0550.1290.1740.1960.3961.0000.1030.561-0.1070.2190.1860.1810.2640.0400.1960.3130.2330.2270.1660.1860.5610.5540.0980.003-0.0060.2550.1750.4300.035-0.013
capx-0.2980.098-0.0230.6130.5670.1031.0000.5070.5520.5770.596-0.0540.7130.5620.7160.5510.4790.954-0.0480.6820.5020.5110.6170.438-0.1070.2080.5800.025-0.215-0.377
ceql-0.2810.1180.0800.7120.8690.5610.5071.0000.7060.6560.6260.2820.7920.4410.7080.7630.6630.6590.2610.6800.9990.9960.5430.424-0.1520.2960.7070.0060.080-0.076
csho-0.3350.052-0.0280.7390.748-0.1070.5520.7061.0000.6590.6510.1980.7870.5470.7420.7010.6370.6610.1740.7160.7050.7090.6170.493-0.2090.1740.732-0.3210.050-0.098
dltt-0.2900.2110.1770.5990.8370.2190.5770.6560.6591.0000.9970.2910.8060.4800.7230.8690.5580.7830.2610.6780.6560.6670.5650.205-0.1420.3910.6590.0030.061-0.056
dt-0.2520.2220.1330.6450.8400.1860.5960.6260.6510.9971.0000.2210.7980.5140.7380.8800.5540.7680.1700.6910.6260.6350.5730.215-0.1370.3740.6760.046-0.036-0.005
dvp-0.0550.0180.1710.0670.3270.181-0.0540.2820.1980.2910.2211.0000.242-0.0370.1260.3180.1170.2240.8450.0420.3000.3010.117-0.140-0.0480.1420.120-0.1050.2700.155
ebitda-0.3420.1560.0460.8140.8810.2640.7130.7920.7870.8060.7980.2421.0000.6780.8540.8490.8400.7960.2210.8830.7920.7940.7080.428-0.2000.4490.8580.117-0.003-0.062
emp-0.3220.067-0.1480.6620.5360.0400.5620.4410.5470.4800.514-0.0370.6781.0000.6880.5490.5840.562-0.0350.8170.4350.4310.6430.286-0.2250.1750.5790.051-0.2540.012
lct-0.3290.158-0.0060.9000.9030.1960.7160.7080.7420.7230.7380.1260.8540.6881.0000.9110.6930.7020.1060.8890.7070.7120.7100.431-0.1790.4110.7580.0690.013-0.074
lt-0.3130.1600.1490.7870.9750.3130.5510.7630.7010.8690.8800.3180.8490.5490.9111.0000.6540.7650.2910.7720.7630.7700.6060.351-0.1770.3680.6960.0030.0640.018
ni-0.2840.142-0.0220.7070.6950.2330.4790.6630.6370.5580.5540.1170.8400.5840.6930.6541.0000.5480.1060.7350.6610.6530.6180.373-0.1970.3710.7860.210-0.044-0.037
ppegt-0.3790.1120.0670.5890.7790.2270.9540.6590.6610.7830.7680.2240.7960.5620.7020.7650.5481.0000.2020.7420.6600.6720.6690.307-0.1920.4190.626-0.016-0.111-0.314
pstkl-0.0640.0140.1440.0600.3010.166-0.0480.2610.1740.2610.1700.8450.221-0.0350.1060.2910.1060.2021.0000.0280.2800.2800.115-0.126-0.0550.1530.115-0.1000.3090.115
revt-0.3470.114-0.0580.8600.7840.1860.6820.6800.7160.6780.6910.0420.8830.8170.8890.7720.7350.7420.0281.0000.6750.6780.7530.374-0.2120.3040.7460.049-0.200-0.141
seq-0.2810.1180.0840.7110.8700.5610.5020.9990.7050.6560.6260.3000.7920.4350.7070.7630.6610.6600.2800.6751.0000.9970.5400.418-0.1500.2990.7040.0050.087-0.070
teq-0.2770.1190.0910.7110.8750.5540.5110.9960.7090.6670.6350.3010.7940.4310.7120.7700.6530.6720.2800.6780.9971.0000.5440.414-0.1450.3020.702-0.0020.080-0.072
xad-0.3120.103-0.0640.6540.6210.0980.6170.5430.6170.5650.5730.1170.7080.6430.7100.6060.6180.6690.1150.7530.5400.5441.0000.324-0.2290.3370.6820.0780.009-0.133
xrd-0.2260.074-0.0000.5250.4010.0030.4380.4240.4930.2050.215-0.1400.4280.2860.4310.3510.3730.307-0.1260.3740.4180.4140.3241.000-0.141-0.0110.494-0.0150.048-0.343
cik0.6660.0270.034-0.178-0.174-0.006-0.107-0.152-0.209-0.142-0.137-0.048-0.200-0.225-0.179-0.177-0.197-0.192-0.055-0.212-0.150-0.145-0.229-0.1411.000-0.219-0.1660.0410.0740.124
dvpsp_f-0.2540.2120.0280.3400.3630.2550.2080.2960.1740.3910.3740.1420.4490.1750.4110.3680.3710.4190.1530.3040.2990.3020.337-0.011-0.2191.0000.3850.324-0.029-0.097
mkvalt-0.2630.290-0.0040.7620.7420.1750.5800.7070.7320.6590.6760.1200.8580.5790.7580.6960.7860.6260.1150.7460.7040.7020.6820.494-0.1660.3851.0000.333-0.024-0.052
prcc_f0.0840.3600.0310.0850.0030.4300.0250.006-0.3210.0030.046-0.1050.1170.0510.0690.0030.210-0.016-0.1000.0490.005-0.0020.078-0.0150.0410.3240.3331.000-0.1030.068
gsector0.1120.0000.015-0.0140.0780.035-0.2150.0800.0500.061-0.0360.270-0.003-0.2540.0130.064-0.044-0.1110.309-0.2000.0870.0800.0090.0480.074-0.029-0.024-0.1031.0000.277
naics0.250-0.0000.060-0.102-0.008-0.013-0.377-0.076-0.098-0.056-0.0050.155-0.0620.012-0.0740.018-0.037-0.3140.115-0.141-0.070-0.072-0.133-0.3430.124-0.097-0.0520.0680.2771.000

Missing values

2023-05-08T00:17:32.019410image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-08T00:17:32.552937image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-05-08T00:17:32.865418image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

gvkeydatadatefyearindfmtconsolpopsrcdatafmtticcusipconmcurcdfyractatbkvlpscapxceqlcshodlttdtdvpebitdaemplctltnippegtpstklrevtseqteqxadxrdcikcostatdvpsp_fmkvaltprcc_fgsectornaics
010452010-12-312010INDLCDSTDAAL02376R102AMERICAN AIRLINES GROUP INCUSD126838.025088.0-11.83091962.0-3945.0333.4509253.011136.00.01303.078.258780.029033.0-471.026717.00.022170.0-3945.0-3945.0165.0NaN6201A0.02597.57557.79020481111
110452011-12-312011INDLCDSTDAAL02376R102AMERICAN AIRLINES GROUP INCUSD126757.023848.0-21.20991610.0-7111.0335.2686702.08220.00.0695.080.108630.030959.0-1979.024854.00.024022.0-7111.0-7111.0186.0NaN6201A0.0117.34380.35020481111
210452012-12-312012INDLCDSTDAAL02376R102AMERICAN AIRLINES GROUP INCUSD127072.023510.0-23.82101888.0-7987.0335.2927116.08535.00.01434.077.759304.031497.0-1876.024233.00.024855.0-7987.0-7987.0153.0NaN6201A0.0266.55710.79520481111
310452013-12-312013INDLCDSTDAAL02376R102AMERICAN AIRLINES GROUP INCUSD1214323.042278.0-10.46083114.0-2731.0261.06915353.016799.00.02956.0110.4013806.045009.0-1834.030392.00.026712.0-2731.0-2731.0166.0NaN6201A0.06591.992325.25020481111
410452014-12-312014INDLCDSTDAAL02376R102AMERICAN AIRLINES GROUP INCUSD1212112.043771.02.89765311.02021.0697.47516196.017904.00.06585.0113.3013435.041750.02882.035343.00.042650.02021.02021.0100.0NaN6201A0.237405.584353.63020481111
510452015-12-312015INDLCDSTDAAL02376R102AMERICAN AIRLINES GROUP INCUSD129985.048415.09.02156151.05635.0624.62218330.020561.00.08891.0118.5013605.042780.07610.040654.00.040990.05635.05635.0110.0NaN6201A0.426452.741742.35020481111
610452016-12-312016INDLCDSTDAAL02376R102AMERICAN AIRLINES GROUP INCUSD1210324.051274.07.46125731.03785.0507.29422489.024344.00.07833.0122.3013872.047489.02676.045353.00.040180.03785.03785.0116.0NaN6201A0.423685.556946.69020481111
710452017-12-312017INDLCDSTDAAL02376R102AMERICAN AIRLINES GROUP INCUSD129146.051396.08.25645971.03926.0475.50822511.025065.00.06809.0126.6014964.047470.01919.049802.00.042207.03926.03926.0135.0NaN6201A0.424740.681252.03020481111
810452018-12-312018INDLCDSTDAAL02376R102AMERICAN AIRLINES GROUP INCUSD128637.060580.0-0.36693745.0-169.0460.61129081.034029.00.05606.0128.9018096.060749.01412.060692.00.044541.0-169.0-169.0128.0NaN6201A0.414790.219232.11020481111
910452019-12-312019INDLCDSTDAAL02376R102AMERICAN AIRLINES GROUP INCUSD128206.059995.0-0.27564268.0-118.0428.20328875.033444.00.06024.0133.7018311.060113.01686.062391.00.045768.0-118.0-118.0129.0NaN6201A0.412280.862028.68020481111
gvkeydatadatefyearindfmtconsolpopsrcdatafmtticcusipconmcurcdfyractatbkvlpscapxceqlcshodlttdtdvpebitdaemplctltnippegtpstklrevtseqteqxadxrdcikcostatdvpsp_fmkvaltprcc_fgsectornaics
48722945242019-12-312019INDLCDSTDLYBN53745100LYONDELLBASELL INDUSTRIES NVUSD129510.030435.024.12162694.08044.0333.47712830.013106.00.05544.019.15198.022256.03390.022728.00.034727.08044.08063.0NaN111.01489393A4.1531506.907094.4815325220
48733160562011-12-312011INDLCDSTDALLEG0176J109ALLEGION PLCUSD12961.72036.2NaN25.51413.8NaN3.54.90.0405.3NaN374.5600.4218.1558.00.02021.21413.81435.8NaN38.91579241ANaNNaNNaN20332510
48743160562012-12-312012INDLCDSTDALLEG0176J109ALLEGION PLCUSD12909.91983.8NaN19.61343.2NaN2.83.70.0419.97.6382.7617.6219.6586.80.02046.61343.21366.2NaN38.21579241ANaNNaNNaN20332510
48753160562013-12-312013INDLCDSTDALLEG0176J109ALLEGION PLCUSD12923.21979.9-0.903920.2-86.896.0291272.01302.00.0409.08.0490.52035.631.0569.20.02093.5-86.8-55.7NaN39.61579241A0.004243.521544.1920332510
48763160562014-12-312014INDLCDSTDALLEG0176J109ALLEGION PLCUSD12973.82015.9-0.050151.5-4.895.8311215.01215.00.0439.38.5531.31997.4175.2586.80.02118.3-4.818.5NaN43.31579241A0.325314.787355.4620332510
48773160562015-12-312015INDLCDSTDALLEG0176J109ALLEGION PLCUSD12735.12285.30.266735.225.695.9911479.81479.80.0444.19.4447.12255.6153.9616.50.02068.125.629.7NaN45.21579241A0.406327.726765.9220332510
48783160562016-12-312016INDLCDSTDALLEG0176J109ALLEGION PLCUSD12829.32247.41.189242.5113.395.2741415.61463.80.0490.09.4429.62131.0229.1640.40.02238.0113.3116.4NaN47.31579241A0.486097.536064.0020332510
48793160562017-12-312017INDLCDSTDALLEG0176J109ALLEGION PLCUSD121032.72542.04.224649.3401.695.0621442.31477.30.0567.310.0460.82136.5273.3707.90.02408.2401.6405.5NaN48.31579241A0.647563.132779.5620332510
48803160562018-12-312018INDLCDSTDALLEG0176J109ALLEGION PLCUSD12931.62810.26.878949.1651.094.6371409.51444.80.0619.011.0520.82156.2434.9748.80.02731.7651.0654.0NaN54.41579241A0.847543.515379.7120332510
48813160562019-12-312019INDLCDSTDALLEG0176J109ALLEGION PLCUSD121001.82967.28.168365.6757.492.7241483.21509.10.0651.311.0507.02206.8401.8867.40.02854.0757.4760.4NaN54.71579241A1.0811547.8470124.5420332510